Establishment and Comprehensive Molecular Characterization of an Immortalized Glioblastoma Cell Line from a Brazilian Patient
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with few effective treatment strategies. The research on the development of new treatments is often constrained by the limitations of preclinical models, which fail to accurately replicate the disease's essential characteristics. Herein, we describe the obtention, molecular, and functional characterization of the GBM33 cell line. This cell line belongs to the GBM class according to the World Health Organization 2021 Classification of Central Nervous System Tumors, identified by methylation profiling. GBM33 expresses the astrocytic marker GFAP, as well as markers of neuronal origin commonly expressed in GBM cells, such as βIII-tubulin and neurofilament. Functional assays demonstrated an increased growth rate when compared to the U87 commercial cell line and a similar sensitivity to temozolamide. GBM33 cells retained response to serum starvation, with reduced growth and diminished activation of the Akt signaling pathway. Unlike LN-18 and LN-229 commercial cell lines, GBM33 is able to produce primary cilia upon serum starvation. In summary, the successful establishment and comprehensive characterization of this GBM cell line provide researchers with invaluable tools for studying GBM biology, identifying novel therapeutic targets, and evaluating the efficacy of potential treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".